Research brief: New technique to select best model subset for extreme projections

Climate models are extremely useful tools for investigating how our climate may change in the future. However, all climate models exhibit biases that must be accounted for, particularly when being used for projections of how extreme events may change.

A number of attempts have been made by researchers to correct for these biases and how they may impact future projections of extreme events. These attempts primarily focus on the distribution shapes simulated by climate models.

CLEX researchers with colleagues from Lawrence Berkeley National Laboratory aimed to test a recently developed quantile-based bias correction scheme in combination with a new method that helps select the optimal subset of climate model simulations to improve projections of extremes.

This optimal subset of climate models most effectively reduces biases in the temperature/rainfall distribution shape, and can subsequently be used for extreme projections. These biases were persistent through time, making them easier to correct for both past and future projections of extremes. The bias correction approaches were also effective at predicting probabilities of exceeding a certain extreme event threshold based on data not used to derive the subset of models.

However, correcting for the shape bias in the climate models did not improve results that were characterized by comparing extremes as ratios over two different periods (so-called probability ratios, commonly used in the field of event attribution).

The researchers used a toy model experiment to determine that the shift in the position of the distribution in response to global warming was at least as important as the shape of the distribution when estimating the probability ratio for extreme weather events.

This study therefore clarifies that it is important to correct biases that are usually identified over static time periods. However, it highlights the importance of simulating global and regional warming responses correctly, to enable more accurate estimates of how the occurrence probability of climate extremes may change in a warming climate.